Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80429
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dc.contributorDepartment of Building and Real Estate-
dc.creatorZhan, X-
dc.creatorCai, Y-
dc.creatorHe, P-
dc.date.accessioned2019-03-26T09:17:08Z-
dc.date.available2019-03-26T09:17:08Z-
dc.identifier.issn1687-8132-
dc.identifier.urihttp://hdl.handle.net/10397/80429-
dc.language.isoenen_US
dc.publisherSAGE Publicationsen_US
dc.rights© The Authors(s) 2018en_US
dc.rightsCreative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work withoutfurther permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).en_US
dc.rightsThe following publication Zhan, X., Cai, Y., & He, P. (2018). A three-dimensional point cloud registration based on entropy and particle swarm optimization. Advances in Mechanical Engineering, 10(12), 1-13 is published by Sage and is available at https://dx.doi.org/10.1177/1687814018814330en_US
dc.subjectK-d treeen_US
dc.subjectEntropyen_US
dc.subjectRobusten_US
dc.titleA three-dimensional point cloud registration based on entropy and particle swarm optimizationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage13-
dc.identifier.volume10-
dc.identifier.issue12-
dc.identifier.doi10.1177/1687814018814330-
dcterms.abstractA three-dimensional (3D) point cloud registration based on entropy and particle swarm algorithm (EPSA) is proposed in the paper. The algorithm can effectively suppress noise and improve registration accuracy. Firstly, in order to find the k-nearest neighbor of point, the relationship of points is established by k-d tree. The noise is suppressed by the mean of neighbor points. Secondly, the gravity center of two point clouds is calculated to find the translation matrix T. Thirdly, the rotation matrix R is gotten through particle swarm optimization (PSO). While performing the PSO, the entropy information is selected as the fitness function. Lastly, the experiment results are presented. They demonstrate that the algorithm is valuable and robust. It can effectively improve the accuracy of rigid registration.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAdvances in mechanical engineering, 3 Dec. 2018, v. 10, no. 12, p. 1-13, https://doi.org/10.1177/1687814018814330-
dcterms.isPartOfAdvances in mechanical engineering-
dcterms.issued2018-
dc.identifier.isiWOS:000452876700001-
dc.identifier.scopus2-s2.0-85058522935-
dc.identifier.eissn1687-8140-
dc.description.validate201903 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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